Overview

Dataset statistics

Number of variables13
Number of observations138791
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 MiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric12

Alerts

Demanda is highly correlated with Month and 6 other fieldsHigh correlation
TMAX-CAB is highly correlated with Demanda and 6 other fieldsHigh correlation
TMAX-HMO is highly correlated with Demanda and 6 other fieldsHigh correlation
TMAX-OBR is highly correlated with Demanda and 6 other fieldsHigh correlation
TMIN-CAB is highly correlated with Demanda and 6 other fieldsHigh correlation
TMIN-HMO is highly correlated with Demanda and 6 other fieldsHigh correlation
TMIN-OBR is highly correlated with Demanda and 6 other fieldsHigh correlation
Month is highly correlated with Demanda and 6 other fieldsHigh correlation
Date has 19847 (14.3%) zeros Zeros
Hour has 5782 (4.2%) zeros Zeros
PREC_HMO (mm) has 120839 (87.1%) zeros Zeros
PREC_OBR (mm) has 122255 (88.1%) zeros Zeros

Reproduction

Analysis started2022-11-07 03:45:01.774779
Analysis finished2022-11-07 03:45:18.986628
Duration17.21 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

FECHA
Date

Distinct5783
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2007-01-01 00:00:00
Maximum2022-10-31 00:00:00
2022-11-06T20:45:19.044970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:19.139329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Demanda
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45638
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2505.570239
Minimum959
Maximum5402.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.234027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum959
5-th percentile1408
Q11867
median2343.74
Q33054.1
95-th percentile4079
Maximum5402.72
Range4443.72
Interquartile range (IQR)1187.1

Descriptive statistics

Standard deviation823.7167817
Coefficient of variation (CV)0.3287542168
Kurtosis-0.2876228818
Mean2505.570239
Median Absolute Deviation (MAD)559.74
Skewness0.6406278244
Sum347750599
Variance678509.3364
MonotonicityNot monotonic
2022-11-06T20:45:19.315052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160082
 
0.1%
178680
 
0.1%
151578
 
0.1%
149776
 
0.1%
157472
 
0.1%
178472
 
0.1%
153772
 
0.1%
157872
 
0.1%
181271
 
0.1%
159071
 
0.1%
Other values (45628)138045
99.5%
ValueCountFrequency (%)
9591
< 0.1%
9661
< 0.1%
9801
< 0.1%
9991
< 0.1%
10011
< 0.1%
10021
< 0.1%
10032
< 0.1%
10041
< 0.1%
10051
< 0.1%
10061
< 0.1%
ValueCountFrequency (%)
5402.721
< 0.1%
53991
< 0.1%
53901
< 0.1%
5348.841
< 0.1%
5336.531
< 0.1%
5297.231
< 0.1%
5295.021
< 0.1%
5290.241
< 0.1%
5289.391
< 0.1%
52861
< 0.1%

Date
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.99950285
Minimum0
Maximum6
Zeros19847
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.379201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.000214279
Coefficient of variation (CV)0.6668486011
Kurtosis-1.250177853
Mean2.99950285
Median Absolute Deviation (MAD)2
Skewness0.0001865299127
Sum416304
Variance4.000857163
MonotonicityNot monotonic
2022-11-06T20:45:19.430031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
019847
14.3%
119824
14.3%
219824
14.3%
319824
14.3%
419824
14.3%
519824
14.3%
619824
14.3%
ValueCountFrequency (%)
019847
14.3%
119824
14.3%
219824
14.3%
319824
14.3%
419824
14.3%
519824
14.3%
619824
14.3%
ValueCountFrequency (%)
619824
14.3%
519824
14.3%
419824
14.3%
319824
14.3%
219824
14.3%
119824
14.3%
019847
14.3%

Hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.50008286
Minimum0
Maximum23
Zeros5782
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.492097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q317.5
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922167599
Coefficient of variation (CV)0.6019232804
Kurtosis-1.204170464
Mean11.50008286
Median Absolute Deviation (MAD)6
Skewness-2.872424259 × 10-6
Sum1596108
Variance47.91640427
MonotonicityNot monotonic
2022-11-06T20:45:19.553118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15783
 
4.2%
25783
 
4.2%
235783
 
4.2%
225783
 
4.2%
215783
 
4.2%
205783
 
4.2%
195783
 
4.2%
185783
 
4.2%
175783
 
4.2%
165783
 
4.2%
Other values (14)80961
58.3%
ValueCountFrequency (%)
05782
4.2%
15783
4.2%
25783
4.2%
35783
4.2%
45783
4.2%
55783
4.2%
65783
4.2%
75783
4.2%
85783
4.2%
95783
4.2%
ValueCountFrequency (%)
235783
4.2%
225783
4.2%
215783
4.2%
205783
4.2%
195783
4.2%
185783
4.2%
175783
4.2%
165783
4.2%
155783
4.2%
145783
4.2%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.470383526
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.611416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.428032772
Coefficient of variation (CV)0.5298036443
Kurtosis-1.196339534
Mean6.470383526
Median Absolute Deviation (MAD)3
Skewness0.003035689015
Sum898031
Variance11.75140869
MonotonicityNot monotonic
2022-11-06T20:45:19.663533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
311904
8.6%
511904
8.6%
711904
8.6%
811904
8.6%
1011904
8.6%
111903
8.6%
411520
8.3%
611520
8.3%
911520
8.3%
1211160
8.0%
Other values (2)21648
15.6%
ValueCountFrequency (%)
111903
8.6%
210848
7.8%
311904
8.6%
411520
8.3%
511904
8.6%
611520
8.3%
711904
8.6%
811904
8.6%
911520
8.3%
1011904
8.6%
ValueCountFrequency (%)
1211160
8.0%
1110800
7.8%
1011904
8.6%
911520
8.3%
811904
8.6%
711904
8.6%
611520
8.3%
511904
8.6%
411520
8.3%
311904
8.6%

TMAX-CAB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2134
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99878551
Minimum9.09
Maximum49.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.733251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.09
5-th percentile19.8
Q126.96
median33.73
Q339.5
95-th percentile44.06
Maximum49.61
Range40.52
Interquartile range (IQR)12.54

Descriptive statistics

Standard deviation7.801089285
Coefficient of variation (CV)0.2364053454
Kurtosis-0.8615582407
Mean32.99878551
Median Absolute Deviation (MAD)6.27
Skewness-0.2962016899
Sum4579934.44
Variance60.85699404
MonotonicityNot monotonic
2022-11-06T20:45:19.815136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401392
 
1.0%
381248
 
0.9%
411200
 
0.9%
261128
 
0.8%
391128
 
0.8%
361104
 
0.8%
231080
 
0.8%
311056
 
0.8%
251056
 
0.8%
371008
 
0.7%
Other values (2124)127391
91.8%
ValueCountFrequency (%)
9.0924
 
< 0.1%
1124
 
< 0.1%
1272
0.1%
12.0924
 
< 0.1%
12.2324
 
< 0.1%
12.324
 
< 0.1%
12.524
 
< 0.1%
12.8124
 
< 0.1%
1372
0.1%
13.124
 
< 0.1%
ValueCountFrequency (%)
49.6124
< 0.1%
49.5324
< 0.1%
48.424
< 0.1%
48.124
< 0.1%
4824
< 0.1%
47.8724
< 0.1%
47.7124
< 0.1%
47.6924
< 0.1%
47.624
< 0.1%
47.5324
< 0.1%

TMAX-HMO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1328
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.55376026
Minimum8
Maximum49.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:19.908652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21.77
Q128.71
median34.5
Q339
95-th percentile43
Maximum49.1
Range41.1
Interquartile range (IQR)10.29

Descriptive statistics

Standard deviation6.634450905
Coefficient of variation (CV)0.1977260031
Kurtosis-0.6405264241
Mean33.55376026
Median Absolute Deviation (MAD)5
Skewness-0.3834708529
Sum4656959.94
Variance44.01593881
MonotonicityNot monotonic
2022-11-06T20:45:19.994155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373888
 
2.8%
393408
 
2.5%
383336
 
2.4%
353336
 
2.4%
403192
 
2.3%
413144
 
2.3%
362760
 
2.0%
422760
 
2.0%
342664
 
1.9%
302568
 
1.9%
Other values (1318)107735
77.6%
ValueCountFrequency (%)
824
 
< 0.1%
14.5824
 
< 0.1%
14.824
 
< 0.1%
15.548
 
< 0.1%
15.724
 
< 0.1%
15.7124
 
< 0.1%
1696
0.1%
16.0124
 
< 0.1%
16.448
 
< 0.1%
16.5144
0.1%
ValueCountFrequency (%)
49.124
 
< 0.1%
48.8624
 
< 0.1%
4824
 
< 0.1%
47.5824
 
< 0.1%
47.572
0.1%
4796
0.1%
46.8624
 
< 0.1%
46.5120
0.1%
46.2324
 
< 0.1%
46.224
 
< 0.1%

TMAX-OBR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1243
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.53774279
Minimum12
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:20.122691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q130
median35.41
Q339.28
95-th percentile43
Maximum47
Range35
Interquartile range (IQR)9.28

Descriptive statistics

Standard deviation5.927637647
Coefficient of variation (CV)0.1716278242
Kurtosis-0.649182679
Mean34.53774279
Median Absolute Deviation (MAD)4.59
Skewness-0.4153157434
Sum4793527.86
Variance35.13688808
MonotonicityNot monotonic
2022-11-06T20:45:20.219182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405856
 
4.2%
385568
 
4.0%
395424
 
3.9%
414992
 
3.6%
374704
 
3.4%
364320
 
3.1%
423792
 
2.7%
293744
 
2.7%
343648
 
2.6%
333600
 
2.6%
Other values (1233)93143
67.1%
ValueCountFrequency (%)
1224
 
< 0.1%
1524
 
< 0.1%
1648
 
< 0.1%
1796
0.1%
17.724
 
< 0.1%
1896
0.1%
18.524
 
< 0.1%
18.8324
 
< 0.1%
19168
0.1%
19.224
 
< 0.1%
ValueCountFrequency (%)
4748
 
< 0.1%
46.524
 
< 0.1%
46.1524
 
< 0.1%
46168
0.1%
45.9724
 
< 0.1%
45.8524
 
< 0.1%
45.724
 
< 0.1%
45.6824
 
< 0.1%
45.548
 
< 0.1%
45.4524
 
< 0.1%

TMIN-CAB
Real number (ℝ)

HIGH CORRELATION

Distinct2169
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.49558574
Minimum-6.8
Maximum32.9
Zeros192
Zeros (%)0.1%
Negative720
Negative (%)0.5%
Memory size1.1 MiB
2022-11-06T20:45:20.340350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-6.8
5-th percentile4
Q110.1
median16
Q323.57
95-th percentile28.78
Maximum32.9
Range39.7
Interquartile range (IQR)13.47

Descriptive statistics

Standard deviation7.921575778
Coefficient of variation (CV)0.4802239766
Kurtosis-1.016973919
Mean16.49558574
Median Absolute Deviation (MAD)6.64
Skewness-0.0117286117
Sum2289438.84
Variance62.75136281
MonotonicityNot monotonic
2022-11-06T20:45:20.467968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221440
 
1.0%
121440
 
1.0%
241368
 
1.0%
101248
 
0.9%
81224
 
0.9%
141200
 
0.9%
91176
 
0.8%
151151
 
0.8%
231104
 
0.8%
261080
 
0.8%
Other values (2159)126360
91.0%
ValueCountFrequency (%)
-6.824
< 0.1%
-5.824
< 0.1%
-5.424
< 0.1%
-524
< 0.1%
-3.224
< 0.1%
-348
< 0.1%
-2.9224
< 0.1%
-2.224
< 0.1%
-2.1324
< 0.1%
-248
< 0.1%
ValueCountFrequency (%)
32.924
< 0.1%
32.8624
< 0.1%
32.524
< 0.1%
32.4824
< 0.1%
32.3624
< 0.1%
32.2124
< 0.1%
32.0624
< 0.1%
32.0224
< 0.1%
32.0124
< 0.1%
31.7824
< 0.1%

TMIN-HMO
Real number (ℝ)

HIGH CORRELATION

Distinct1338
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.50379463
Minimum-3
Maximum34
Zeros24
Zeros (%)< 0.1%
Negative24
Negative (%)< 0.1%
Memory size1.1 MiB
2022-11-06T20:45:20.588659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile7.09
Q113
median18
Q325
95-th percentile29
Maximum34
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.05737714
Coefficient of variation (CV)0.3814016143
Kurtosis-1.049131135
Mean18.50379463
Median Absolute Deviation (MAD)6
Skewness-0.1101890143
Sum2568160.16
Variance49.8065721
MonotonicityNot monotonic
2022-11-06T20:45:20.723530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
274344
 
3.1%
263624
 
2.6%
253600
 
2.6%
113240
 
2.3%
163239
 
2.3%
283120
 
2.2%
133000
 
2.2%
142952
 
2.1%
222880
 
2.1%
122784
 
2.0%
Other values (1328)106008
76.4%
ValueCountFrequency (%)
-324
< 0.1%
024
< 0.1%
0.3724
< 0.1%
0.4624
< 0.1%
0.6224
< 0.1%
0.6624
< 0.1%
0.6824
< 0.1%
0.724
< 0.1%
1.0624
< 0.1%
1.2324
< 0.1%
ValueCountFrequency (%)
3424
 
< 0.1%
33.424
 
< 0.1%
33.324
 
< 0.1%
32168
 
0.1%
31.824
 
< 0.1%
31.7324
 
< 0.1%
31.548
 
< 0.1%
31.3924
 
< 0.1%
31.0724
 
< 0.1%
31720
0.5%

TMIN-OBR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1304
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.66678272
Minimum2
Maximum42.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:20.854392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.22
Q113
median18
Q325
95-th percentile29
Maximum42.5
Range40.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.737229591
Coefficient of variation (CV)0.36092077
Kurtosis-1.139080852
Mean18.66678272
Median Absolute Deviation (MAD)6
Skewness0.02303209835
Sum2590781.44
Variance45.39026256
MonotonicityNot monotonic
2022-11-06T20:45:20.944845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265328
 
3.8%
254776
 
3.4%
144559
 
3.3%
274464
 
3.2%
244440
 
3.2%
104128
 
3.0%
133984
 
2.9%
163936
 
2.8%
153864
 
2.8%
113792
 
2.7%
Other values (1294)95520
68.8%
ValueCountFrequency (%)
248
< 0.1%
2.624
< 0.1%
324
< 0.1%
3.324
< 0.1%
3.8824
< 0.1%
448
< 0.1%
4.0424
< 0.1%
4.124
< 0.1%
4.3424
< 0.1%
4.4824
< 0.1%
ValueCountFrequency (%)
42.524
 
< 0.1%
3324
 
< 0.1%
32144
0.1%
31.9624
 
< 0.1%
31.9224
 
< 0.1%
31.6724
 
< 0.1%
31.6224
 
< 0.1%
31.5624
 
< 0.1%
31.5124
 
< 0.1%
31.4124
 
< 0.1%

PREC_HMO (mm)
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.398324099
Minimum0
Maximum117
Zeros120839
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:21.035888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.2
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.939963432
Coefficient of variation (CV)4.963057876
Kurtosis76.87536046
Mean1.398324099
Median Absolute Deviation (MAD)0
Skewness7.721436168
Sum194074.8
Variance48.16309244
MonotonicityNot monotonic
2022-11-06T20:45:21.124175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120839
87.1%
0.013312
 
2.4%
1480
 
0.3%
0.3384
 
0.3%
1.2360
 
0.3%
0.5336
 
0.2%
0.1312
 
0.2%
2288
 
0.2%
0.2288
 
0.2%
3264
 
0.2%
Other values (242)11928
 
8.6%
ValueCountFrequency (%)
0120839
87.1%
0.013312
 
2.4%
0.0424
 
< 0.1%
0.1312
 
0.2%
0.2288
 
0.2%
0.3384
 
0.3%
0.4192
 
0.1%
0.5336
 
0.2%
0.696
 
0.1%
0.7144
 
0.1%
ValueCountFrequency (%)
11724
< 0.1%
11524
< 0.1%
103.924
< 0.1%
92.124
< 0.1%
83.524
< 0.1%
80.524
< 0.1%
8024
< 0.1%
72.524
< 0.1%
7224
< 0.1%
71.824
< 0.1%

PREC_OBR (mm)
Real number (ℝ≥0)

ZEROS

Distinct184
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.166726373
Minimum0
Maximum166.8
Zeros122255
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-06T20:45:21.214298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5
Maximum166.8
Range166.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.680982958
Coefficient of variation (CV)5.726263771
Kurtosis141.1251942
Mean1.166726373
Median Absolute Deviation (MAD)0
Skewness10.03783287
Sum161931.12
Variance44.63553329
MonotonicityNot monotonic
2022-11-06T20:45:21.307959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122255
88.1%
0.012856
 
2.1%
11440
 
1.0%
0.5936
 
0.7%
2720
 
0.5%
1.5552
 
0.4%
3480
 
0.3%
2.5384
 
0.3%
5336
 
0.2%
9264
 
0.2%
Other values (174)8568
 
6.2%
ValueCountFrequency (%)
0122255
88.1%
0.012856
 
2.1%
0.0524
 
< 0.1%
0.1144
 
0.1%
0.2216
 
0.2%
0.3240
 
0.2%
0.424
 
< 0.1%
0.5936
 
0.7%
0.696
 
0.1%
0.724
 
< 0.1%
ValueCountFrequency (%)
166.824
< 0.1%
10424
< 0.1%
10224
< 0.1%
10124
< 0.1%
91.724
< 0.1%
9124
< 0.1%
8524
< 0.1%
8124
< 0.1%
80.624
< 0.1%
7424
< 0.1%

Interactions

2022-11-06T20:45:17.262726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.247804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.390584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.457003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.805714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.846667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.905050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.899222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.031467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.083539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.140779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.154006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.347909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.360100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.473521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.532749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.884854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.932632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.987087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.978096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.113244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.175965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.218376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.243714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.429486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.505509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.553492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.615897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.973193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.022169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.068866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.058198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.203877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.264408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.301011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.327848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.507939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.606858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.644393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.695371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.057806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.102154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.144150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.132340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.282167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.348474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.387251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.413373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.593156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.697847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.731268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.783197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.135612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.188210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.232189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.226039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.368849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.446909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.481880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.500354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.677974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.785108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.828841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.877217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.224919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.283245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.325614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.320802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.461026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.546937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.577860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.593709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.755044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:05.859738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.909571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.951822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.312692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.365636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.408368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.408834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.543212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-06T20:45:15.653433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-06T20:45:17.842652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-06T20:45:07.009508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.368350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.399078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.452586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.495657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.486277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.625573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.720034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.732957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.764550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:18.105372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.032853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.088488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.454988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.491318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.543187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.575636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.566012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.717164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.800606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-06T20:45:16.865570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:18.193000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.120824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.168964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.538693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.582339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.637245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.651438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.784010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.809199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.882551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.894078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.949209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:18.281280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.212593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.276607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.629546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.675209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.728207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.730154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.868056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.897294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:14.966133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.985736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.052539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:18.368729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:06.300426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:07.363832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:08.717073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:09.756070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:10.813283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:11.818624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:12.948637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:13.986994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:15.054390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:16.069025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:45:17.151155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-06T20:45:21.399572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-06T20:45:21.526335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-06T20:45:21.644550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-06T20:45:21.759188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-06T20:45:18.486902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-06T20:45:18.745213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FECHADemandaDateHourMonthTMAX-CABTMAX-HMOTMAX-OBRTMIN-CABTMIN-HMOTMIN-OBRPREC_HMO (mm)PREC_OBR (mm)
02007-01-011297.001121.021.525.02.09.07.50.00.0
12007-01-011255.002121.021.525.02.09.07.50.00.0
22007-01-011222.003121.021.525.02.09.07.50.00.0
32007-01-011168.004121.021.525.02.09.07.50.00.0
42007-01-011128.005121.021.525.02.09.07.50.00.0
52007-01-011100.006121.021.525.02.09.07.50.00.0
62007-01-011083.007121.021.525.02.09.07.50.00.0
72007-01-011076.008121.021.525.02.09.07.50.00.0
82007-01-011022.009121.021.525.02.09.07.50.00.0
92007-01-011029.0010121.021.525.02.09.07.50.00.0

Last rows

FECHADemandaDateHourMonthTMAX-CABTMAX-HMOTMAX-OBRTMIN-CABTMIN-HMOTMIN-OBRPREC_HMO (mm)PREC_OBR (mm)
1387812022-10-312834.870141029.031.531.515.016.014.00.00.0
1387822022-10-312914.070151029.031.531.515.016.014.00.00.0
1387832022-10-312990.750161029.031.531.515.016.014.00.00.0
1387842022-10-313038.080171029.031.531.515.016.014.00.00.0
1387852022-10-313014.080181029.031.531.515.016.014.00.00.0
1387862022-10-312934.740191029.031.531.515.016.014.00.00.0
1387872022-10-312926.850201029.031.531.515.016.014.00.00.0
1387882022-10-312894.380211029.031.531.515.016.014.00.00.0
1387892022-10-312877.560221029.031.531.515.016.014.00.00.0
1387902022-10-312843.080231029.031.531.515.016.014.00.00.0